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AI Research
arXiv CS.AI·Jun 10·5 min read
Rotate2Think: Geometric Priming via Orthogonal Rotation to Improve Language Model Reasoning
TL;DR
Language models struggle to effectively transition from input prompts to reasoning outputs. Rotate2Think is a method that uses orthogonal rotation to enhance this transition by injecting a synthetic thinking vector during inference.
✦ Why It Matters
Engineers can leverage Rotate2Think to enhance reasoning accuracy in language models without additional training.
Key Takeaways
- Explore geometric priming techniques for model enhancement.
- Utilize orthogonal Procrustes analysis for embedding alignment.
- Test Rotate2Think across various model configurations.
- Apply findings to improve reasoning in multimodal tasks.
How It Works
Rotate2Think leverages the observation that input and reasoning embeddings cluster around distinct mean directions in the representation space. By applying orthogonal Procrustes analysis, it calculates a rotation that aligns these embeddings, allowing for the creation of a synthetic thinking vector.
This vector is then injected into the model during inference, acting as a geometric primer that enhances the reasoning process.